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Introduction. White spot lesions are early signs of demineralization, which may or may not lead to the development of caries. An inactive white spot lesion might act as an arrested dental caries and affect the esthetic appearance by displaying a milky white color. These lesions need a non-invasive or a minimally invasive treatment. One of the materials taken into consideration in treating these lesions is ICON, a biomimetic material that infiltrates the white spots and can reestablish the esthetic function. The aim of the current study was to evaluate the esthetic efficacy of a minimally invasive technique and a relatively new product on the dental market, ICON, in the treatment of different non-cavitated carious lesions. Case presentation. A 14-year-old patient presented to the dental clinic with asymptomatic, non-cavitating lesions on the cervical aspect of all permanent teeth. The lesions were diagnosed as incipient caries on the buccal, occlusal, and palatal surfaces of the teeth, and the proposed treatment to arrest their progression and improve the esthetic appearance was the use of the ICON infiltration technique. Conclusion. The ICON resin infiltration treatment increased esthetics, visibly diminishing the appearance of non-cavitating carious lesions.

Dental sealants are excellent means to prevent pits and fissure caries. Nowadays, the application of sealants is extended to therapeutic use in arresting non-cavitated carious lesions. This relatively new concept still lacks evidence to support its routine use. The aim of this study was to evaluate the effectiveness of a resin-based sealant applied on first permanent molars with carious lesions (ICDAS 1–3), in comparison with its effectiveness when applied on sound surfaces (ICDAS 0). Included in the study were 114 children aged between six and eight years old, with a high caries risk (according to the CAMBRA system), with all four permanent molars erupted and with deep pits and fissures. A total number of 407 molars were sealed and assessed. A total of 49 were excluded (they had caries, which according to the ICDAS II classification were classified with code 4–6 or had older sealants or fillings). Out of these 407 molars, 213 were sound (code 0) and 194 had caries lesions as follows: 56 teeth classified as code 1, 79 teeth classified as code 2, and 59 teeth classified as code 3 according to the ICDAS II classification. The retention of the sealant and carious lesions were assessed clinically at 6, 12, 18, and 24 months. Regarding sealant retention, a statistically significant difference (p < 0.05) among the two types of sealed teeth, sound (ICDAS 0) and decayed (ICDAS 3), showed at 18- and 24-month follow-up intervals. Regarding caries lesions, a statistically significant difference (p < 0.05) showed between sound (ICDAS 0) and decayed (ICDAS 3) molars at 24-month follow-up. Our study results supported the resin-based sealant effectiveness in arresting incipient carious lesions, which according to the ICDAS II classification have received codes 1 and 2 but did not support sealant effectiveness in arresting caries lesions classified according to the same classification with code 3.

Oral health is a critical indicator of children’s quality of life, which at this early age, depends exclusively on the attention, involvement, and guidance of parents or caregivers. Assessing carious damage and measuring the prevalence of early severe tooth decay in temporary teeth in children is obtained by calculating the dmft index, giving the sum of an individual’s decayed, missing, and filled teeth. The aim of our study was to conduct a clinical examination of the oral status of institutionalized children from Romania. We selected and included in the study 144 children, both boys and girls in equal number, with ages of five or six years old, from which 110 were eligible for the study and met the inclusion criteria. Of all children, 20.90% were five years old girls, 27.27% five years old boys, 26.36% six years old girls, and 25.46% six years old boys. Of all, 10,45% had more than three incisors presenting decays, equally affected by gender. Of the boys’ group, 20% of age six had more de two canines affected, and 26.7% of five years old girls had more than three affected canines. Of the five years boys’ group, 24.3% had more than three affected temporary molars, 44.9% of six years boys had more than five. Of the five years old girls, 33.3% had more than four temporary molars affected and, 56.6% of the six years old girls’ group had more than five molars presenting carious lesions. The dental status and dmft values were similar for five years old and six years old children and similar boys and girls. Due to the vulnerability of young children that consists not only in their inability to identify, express, and address their own needs but also the lack of parental support, lack of an optimal diet for age, and proper hygiene, they reach adolescence with an impaired dental status, inappropriate for their age.

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies.

BackgroundThe COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil’s social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically.MethodsWe consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics.FindingsThe XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95%CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors.InterpretationSocioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.FundingNone.

Motivation Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In this study, we introduce the use of graph neural networks for the unsupervised exploration of scRNA-seq data by developing a variational graph autoencoder architecture with graph attention layers that operates directly on the connectivity between cells, focusing on dimensionality reduction and clustering. With the help of several case studies, we show that our model, named CellVGAE, can be effectively used for exploratory analysis even on challenging datasets, by extracting meaningful features from the data and providing the means to visualize and interpret different aspects of the model. Results We show that CellVGAE is more interpretable than existing scRNA-seq variational architectures by analysing the graph attention coefficients. By drawing parallels with other scRNA-seq studies on interpretability, we assess the validity of the relationships modelled by attention, and furthermore, we show that CellVGAE can intrinsically capture information such as pseudotime and NF-ĸB activation dynamics, the latter being a property that is not generally shared by existing neural alternatives. We then evaluate the dimensionality reduction and clustering performance on 9 difficult and well-annotated datasets by comparing with three leading neural and non-neural techniques, concluding that CellVGAE outperforms competing methods. Finally, we report a decrease in training times of up to × 20 on a dataset of 1.3 million cells compared to existing deep learning architectures. Availabilityand implementation The CellVGAE code is available at https://github.com/davidbuterez/CellVGAE. Supplementary information Supplementary data are available at Bioinformatics online.

Selecting causal inference models for estimating individualized treatment effects (ITE) from observational data presents a unique challenge since the counterfactual outcomes are never observed. The problem is challenged further in the unsupervised domain adaptation (UDA) setting where we only have access to labeled samples in the source domain, but desire selecting a model that achieves good performance on a target domain for which only unlabeled samples are available. Existing techniques for UDA model selection are designed for the predictive setting. These methods examine discriminative density ratios between the input covariates in the source and target domain and do not factor in the model's predictions in the target domain. Because of this, two models with identical performance on the source domain would receive the same risk score by existing methods, but in reality, have significantly different performance in the test domain. We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain. Experimentally, our method selects ITE models that are more robust to covariate shifts on several healthcare datasets, including estimating the effect of ventilation in COVID-19 patients from different geographic locations.

Summary Background Brazil ranks second worldwide in total number of COVID-19 cases and deaths. Understanding the possible socioeconomic and ethnic health inequities is particularly important given the diverse population and fragile political and economic situation. We aimed to characterise the COVID-19 pandemic in Brazil and assess variations in mortality according to region, ethnicity, comorbidities, and symptoms. Methods We conducted a cross-sectional observational study of COVID-19 hospital mortality using data from the SIVEP-Gripe (Sistema de Informacao de Vigilância Epidemiologica da Gripe) dataset to characterise the COVID-19 pandemic in Brazil. In the study, we included hospitalised patients who had a positive RT-PCR test for severe acute respiratory syndrome coronavirus 2 and who had ethnicity information in the dataset. Ethnicity of participants was classified according to the five categories used by the Brazilian Institute of Geography and Statistics: Branco (White), Preto (Black), Amarelo (East Asian), Indigeno (Indigenous), or Pardo (mixed ethnicity). We assessed regional variations in patients with COVID-19 admitted to hospital by state and by two socioeconomically grouped regions (north and central-south). We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation. Findings Of 99 557 patients in the SIVEP-Gripe dataset, we included 11 321 patients in our study. 9278 (82·0%) of these patients were from the central-south region, and 2043 (18·0%) were from the north region. Compared with White Brazilians, Pardo and Black Brazilians with COVID-19 who were admitted to hospital had significantly higher risk of mortality (hazard ratio [HR] 1·45, 95% CI 1·33–1·58 for Pardo Brazilians; 1·32, 1·15–1·52 for Black Brazilians). Pardo ethnicity was the second most important risk factor (after age) for death. Comorbidities were more common in Brazilians admitted to hospital in the north region than in the central-south, with similar proportions between the various ethnic groups. States in the north had higher HRs compared with those of the central-south, except for Rio de Janeiro, which had a much higher HR than that of the other central-south states. Interpretation We found evidence of two distinct but associated effects: increased mortality in the north region (regional effect) and in the Pardo and Black populations (ethnicity effect). We speculate that the regional effect is driven by increasing comorbidity burden in regions with lower levels of socioeconomic development. The ethnicity effect might be related to differences in susceptibility to COVID-19 and access to health care (including intensive care) across ethnicities. Our analysis supports an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect Pardo and Black Brazilians, as well as the population of poorer states, from their higher risk of dying of COVID-19. Funding None.

Background The COVID-19 pandemic is quickly spreading throughout Brazil, which is rapidly ascending the ranking of countries with the highest number of cases and deaths. A particularly unstable federal regime and fragile socioeconomic situation is likely to have contributed to the impact of the disease. Amid this crisis there is substantial concern in the possible socioeconomic, geopolitical and ethnic inequity of the impact of COVID-19 on the country’s particularly diverse population. Methods We performed a cross-sectional observational study of COVID-19 hospital mortality using observational data from the SIVEP-Gripe dataset. We present descriptive statistics to quantify the COVID-19 pandemic in Brazil. We assess the importance of regional factors such as education, income and health either on a state-by-state basis or by splitting Brazil into a North and a Central-South region. Mixed-effects survival analysis was used to estimate the effects of ethnicity and comorbidity at an individual level in the context of regional variation. Findings Our results show that, compared to branco comparators, hospitalised pardo and preto Brazilians have significantly higher risk of mortality, with hazard ratios and 95% CI of 1.47 (1.33-1.58) and 1.32 (1.15-1.52), respectively. In particular, pardo ethnicity was the second most important risk factor (after age). We also found that hospitalised Brazilians in North regions tend to have more comorbidities than in the Central-South, with similar proportions between the various ethnic groups. Finally, we found that states in the North have a higher hazard ratio as compared to the Central-South, and that Rio de Janeiro obtained one of the highest hazard ratios, similar to the ones of the more underdeveloped Pernambuco and Amazonas. Interpretation Our results can be interpreted according to the interplay of two independent, but correlated, effects: i) mortality by COVID-19 increases going North (vertical effect), ii) mortality increases for the pardo and preto population (horizontal effect). We speculate that the vertical effect is driven by increasing levels of comorbidity in Northern regions where levels of socioeconomic development are lower, whereas the horizontal effect may be related to lower levels of healthcare access or availability (including intensive care) for pardo and preto Brazilians. For most states the vertical and horizontal effects are correlated giving a larger cumulative mortality. However, Rio de Janeiro was found to be an outlier to this trend: It has an ethnicity composition (horizontal effect) similar to the states in the North region, despite high levels of socioeconomic development (vertical effect). Our analysis motivates an urgent effort on the part of Brazilian authorities to consider how the national response to COVID-19 can better protect pardo and preto Brazilians as well as the population of poorer states from their higher death risk from SARS-CoV-2 infection. Funding None.

Aim of the study. In our study we evaluated, by using a three-dimensional Finite Element Model, the effect of intrusive and extrusive forces applied during orthodontic treatment. Material and methods. A tridimensional image of the tooth was obtained, by creating a model with 1379 nodes and 976 finite elements. We selected two clinical situations frequently present during orthodontic treatment: the use of a force aiming to align the teeth according to the vertical dimension, through extrusion or intrusion, which exerts a compression or elongation effect on the periodontal complex. Results. The results showed that the use of high force determined an increased stress especially in the apical third of the root. Conclusions. The use of high forces could determine an increased stress in the apical third of the root and disturbances in pulp circulation or even loss of dental pulp vitality.

Ice-templated collagen-based tissue-engineering scaffolds are ideal for controlled tissue regeneration since they mimic the micro-environment experienced in vivo. The structure and properties of scaffolds are fine-tuned during fabrication by controlling a number of experimental parameters. However, this parameter space is large and complex, rendering the interpretation of results and selection of optimal parameters to be challenging in practice. This paper investigates the impact of a cross section of this parameter space (drying conditions and solute environment) on the scaffold microstructure. Qualitative assessment revealed the previously unreported impact of drying temperature and pressure on pore wall roughness, and confirmed the influence of collagen concentration, solvent type, and solute addition on pore morphology. For quantitative comparison, we demonstrate the novel application of random forest regression to analyze multi-dimensional biomaterials datasets, and predict microstructural attributes for a scaffold. Using these regression models, we assessed the relative importance of the input experimental parameters on quantitative pore measurements. Collagen concentration and pH were found to be the largest factors in determining pore size and connectivity. Furthermore, circular dichroism peak intensities were also revealed to be a good predictor for structural variations, which is a parameter that has not previously been investigated for its effect on a scaffold microstructure. Thus, this paper demonstrates the potential for predictive models such as random forest regressors to discover novel relationships in biomaterials datasets. These relationships between parameters (such as circular dichroism spectra and pore connectivity) can therefore also be used to identify and design further avenues of investigation within biomaterials.

Background. The Centers for Disease Control and Prevention has emphasized the need for interventional programs regarding hepatitis C virus (HCV) infection for injection drug users, the group of persons who are at highest risk of acquiring acute infection. Methods. We designed a pilot study to assess the feasibility of identifying injection drug users with acute HCV infection in correctional and detoxification facilities. On-site medical providers were educated regarding risk factors and signs and symptoms of infection and were instructed to refer all patients with hepatitis to our specialty clinic. Results. Over a 30-month period, 21 patients received a diagnosis of acute hepatitis C, 3 received a diagnosis of hepatitis B, and 1 received a diagnosis of hepatitis A. Of the 21 patients with acute hepatitis C, 19 were identified in the prison setting shortly after incarceration. Of the 17 patients who were observed serially (mean duration of observation, 6.3 months), 8 had spontaneous virologic clearance. Early therapy with pegylated interferon was initiated for 5 patients with persistent viremia and led to a sustained virologic response in 2 individuals. All patients agreed to undergo human immunodeficiency virus counseling and testing, as well as to receive immunization for hepatitis A and B. Conclusions. Incarceration presents a unique opportunity to identify injection drug users with acute HCV infection, to initiate counseling regarding other bloodborne pathogens, and to facilitate immunizations and HCV treatment.

Highly active antiretroviral therapy has decreased human immunodeficiency virus (HIV)-associated mortality; other comorbidities, such as chronic liver disease, are assuming greater importance. We retrospectively examined the causes of death of HIV-seropositive patients at our institution in 1991, 1996, and 1998-1999. In 1998-1999, 11 (50%) of 22 deaths were due to end-stage liver disease, compared with 3 (11.5%) of 26 in 1991 and 5 (13.9%) of 36 in 1996 (P=.003). In 1998-1999, 55% of patients had nondetectable plasma HIV RNA levels and/or CD4 cell counts of >200 cells/mm(3) within the year before death. Most of the patients that were tested had detectable antibodies to hepatitis C virus (75% of patients who died in 1991, 57.7% who died in 1996, and 93.8% who died in 1998-1999; P=NS). In 1998-1999, 7 patients (31.8%) discontinued antiretroviral therapy because of hepatotoxicity, compared with 0 in 1991 and 2 (5.6%) in 1996. End-stage liver disease is now the leading cause of death in our hospitalized HIV-seropositive population.

We describe a patient who developed daptomycin-resistant, methicillin-resistant Staphylococcus aureus (MRSA) during an episode of presumed septic thrombophlebitis of the portal vein. Although daptomycin is an alternative agent for treatment of drug-resistant gram-positive bacterial infections, development of resistance during prolonged use may occur with MRSA bacteremia from a persistent focus.

We examined the association between metabolic syndrome (MS) and its individual defining criteria on all-cause mortality in human immunodeficiency virus (HIV)-infected persons. We used data from 567 HIV-infected participants of the Nutrition for Healthy Living study with study visits between 9/1/2000 and 1/31/2004 and determined mortality through 12/31/2006. MS was defined using modified National Cholesterol Education Program guidelines. Cox proportional hazards for all-cause mortality were estimated for baseline MS status and for its individual defining criteria. There were 83 deaths with median follow-up of 63 months. Baseline characteristics associated with increased risk of mortality were: older age in years (univariate hazard ratio [HR] 1.04, p

Background. Human immunodeficiency virus (HIV)–infected patients with hepatitis C virus (HCV) coinfection are at increased risk for drug-induced liver injury (DILI) compared with patients with HIV infection alone. The mechanism underlying this observation is unknown. We hypothesized that interferon (IFN) would induce biochemical improvement through its anti-inflammatory properties and thereby facilitate the reintroduction of antiretroviral therapy (ART) in patients with DILI. Methods. Patients with symptomatic DILI were referred for evaluation; biopsy of a liver sample was performed for all patients, except 1 with clinical cirrhosis. Results. Twelve patients with acquired immunodeficiency syndrome and symptomatic grade 3/4 hepatotoxicity received treatment with IFN and ribavirin (RBV). Seven of these patients had a history of recurrent DILI. The mean baseline CD4 + T cell counts and HIV RNA levels were 124 cells/mm 3 and 115,369 copies/mL, respectively. Biopsies of liver samples demonstrated significant necroinflammation (mean grade, 10.3) and fibrosis (mean stage, 2.9). Three patients continued to receive ART when they began treatment with IFN-RBV; 9 reinitiated ART within an average of 12 weeks (range, 4–20 weeks) of HCV treatment initiation. All patients attained marked improvement in aminotransferases and continued to receive ART treatment during a mean follow-up regimen of 26.5 months, with subsequent virologic suppression and immunologic reconstitution (mean CD4 + cell count increase, 251/mm 3 ). However, only 1 patient maintained HCV suppression after completion of treatment with IFN-RBV. Conclusions. In patients with symptomatic DILI, treatment with IFN-ribavirin (RBV) led to decreases in aminotransferase levels, which enabled the reinitiation of ART. The beneficial effects of IFN-based therapy may be modulated through the suppression of proinflammatory cytokines, even in virologic nonresponders. Herein, we propose a novel mechanism for DILI, whereby HCV- and HIV-associated inflammatory mediators induce liver injury synergistically.

The increase in morbidity and mortality due to end-stage liver disease has fueled recent guidelines that recommend consideration of treatment for hepatitis C in human immunodeficiency virus (HIV)-infected patients. Unfortunately, studies indicate that few patients coinfected with HIV and hepatitis C virus (HCV) are treated for their underlying hepatitis because of ongoing substance abuse, depression, chaotic lifestyles, homelessness, and perceived nonadherence. The structured environment of the prison system enables clinicians to provide complicated therapies for HCV to HIV-infected patients in combination with substance abuse programs. Furthermore, adherence to and adverse effects of therapy can be closely monitored. Offering treatment for HCV infection during incarceration to HIV-seropositive persons is highly efficient and targets underserved minority patients who have limited access to care in the community.

In human immunodeficiency virus (HIV) disease, symptoms of underlying illness may promote weight loss through decreased caloric intake, increased metabolic needs, or nutrient malabsorption. We evaluated disease symptoms as predictors of acute weight loss (i.e., loss of > or =5% of weight). HIV-infected men and women (n=415) were telephoned every 5 weeks to obtain information about weight and recent symptoms. Weight change between each pair of consecutive calls (telephone intervals, 2814) was calculated. Acute weight loss occurred across 4.5% of intervals and among 24% of individuals. Patients reported > or =1 symptom before 58% of telephone intervals. The most common symptoms or symptom complexes before intervals were diarrhea (21% of patients), anorexia (17%), upper respiratory symptoms (16%), skin symptoms (12%), and abdominal pain (12%). Trouble swallowing (6%) and oral symptoms (7%) were less common. Risk of acute weight loss was significantly increased when oral symptoms or trouble swallowing were present, and it was decreased when highly active antiretroviral therapy (HAART) was used or when diarrhea was not present. Even when HAART is being administered, clinicians should remain vigilant regarding weight loss, oral symptoms, and trouble swallowing.